Metabolic Pathways in Hydrocephalus: Profiling with Proteomics and Advanced Imaging

Abstract

Hemorrhagic hydrocephalus is a common pathology in neonates with high mortality and morbidity. Current imaging approaches fail to capture the mechanisms behind its pathogenesis. Here, we discuss the processes underlying this pathology, the metabolic dysfunction that occurs as a result, and the ways in which these metabolic changes inform novel methods of clinical imaging. The imaging advances described allow earlier detection of the cellular and metabolic changes, leading to better outcomes for affected neonates.

Keywords: contrast-enhanced ultrasound; hydrocephalus; proteomics; super-resolution imaging.

Figure 1

Summary of pathways involved in hemorrhagic hydrocephalus. Damage-associated molecular patterns (DAMPs) activating toll-like receptors (TLRs), as well as leading to inflammatory cell recruitment, causing (1) reactive oxygen species generation; (2) receptor-activating protein kinase 3 (RIPK3) activation with resultant necroptosis; (3) NOD-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome activation and resultant pyroptosis, Gasdermin D activation leading to apoptosis, and phosphorylation of sodium, potassium chloride co-transporter-1 (NKCCL1) by the NLRP3 inflammasome complex in the choroid plexus epithelium, resulting in CSF hypersecretion (GSDMD: Gasdermin D; NFK$\beta$: nuclear factor kappa beta; ROS: reactive oxygen species (created with BioRender.com, accessed on 12 June 2024)).

Figure 2

Ultrasound elastography of a 2-month-old, former 28-week and 4-day-old male infant following shunt placement for treatment of post-hemorrhagic hydrocephalus. (A) Grayscale coronal ultrasound image showing a catheter placed in the right lateral ventricle. Cystic change is present in the right periventricular white matter in keeping with prior infarct. Following shunt placement, elastography was monitored and showed (B) the right basal ganglia, with a value of 1.55 m/s and (C) the periventricular white matter with a value of 2.18 m/s. (Images under creative commons license, https://creativecommons.org/licenses/by-nc/4.0/, accessed on 25 July 2024) [68].

Figure 3

Series of images in a 22-day-old preterm female with hydrocephalus secondary to intraventricular hemorrhage. (A) shows the coronal view in grayscale at the level of the lateral ventricles, showing a marked increase in ventricular size and layering blood products within the ventricles. (B,C) show microvascular imaging of the slow-flowing vessels in coronal (B) and sagittal (C) planes, with relative velocity shown by faster flow in yellow and less fast in orange to red (images under creative commons license https://creativecommons.org/licenses/by-nc/4.0/ accessed on 25 July 2024 [68]).

Figure 4

(a) Cerebral blood vessels (top row) and the corresponding velocity distributions (bottom row) for piglet 0121, demonstrating the effect of increasing intracranial pressure on the cerebral perfusion as detected by super-resolution ultrasound. (b–d): Relationships between intracranial pressure and the cerebral microcirculation (CMC, original cohort) parameters in the thalamus (b), in cortical subregion 2 (c), and in all three cortical subregions combined ((d), CMCcort). (e) Relationships between the intracranial pressure and the time-averaged velocity (V5) in the macro blood vessel are marked with a blue star in (a). For (b–e), data are presented as mean +/− SD, where the error bars show the temporal standard deviations of CMCs or V5 over 20 cardiac phases; several standard deviation values for high ICP are too small to be shown. The curves are least-square fitted quadratic functions, with the corresponding coefficient of determination (R2) shown on each plot. Also presented are the correlation coefficients (r) and the corresponding p values between the CMCs or V5 and the ICP. Images under creative commons license, http://creativecommons.org/licenses/by/4.0/ (accessed on 24 May 2024) [78].